Noise assessment is important to image quality evaluation. When an image is said to be noisier or less noisy than other images, a certain determination on the image noise level is made based upon either a subjective visual evaluation or a predetermined metric. In this regard, the prior art attempts provided elaborate manual measurements of the noise level in regions of interest. Subjective evaluation is time-consuming and expensive and lacks uniform criteria. In other words, subjective evaluation is operator dependent. Furthermore, the manual noise assessment techniques are also not generally compatible with an automatic process for the subjective evaluation.
In the prior art attempts, a single indiscriminate index has been provided for spatially variant noise, and the noise index is not fully representative of an entire image. For example, even if a predetermined common metric such as standard deviation (SD) is deduced from an image, SD still lacks reliable results in noise assessment for certain image. In this regard, one exemplary technique applied Laplacian to images for removing edge pixels according to edge detection, and noise was assessed by either average or variance from the remaining pixels of the Laplacian result. The Laplacian methods measured noise from the manipulated image, that is, not from image directly.
In other prior art automatic noise assessment techniques, although noise is assumed to be dependent on imaging modalities, its noise models were complicated. Other simpler prior art automatic noise assessment techniques such as a twin image subtraction method required two reconstructions from odd and even views of the CT acquisition sequence, which makes it not be viable for iterative processing, where image noise change from iteration to iteration.
In view of the prior art noise assessment techniques, a method and a system of reliably determining a noise level is desired without human intervention for substantially all imaging modalities without requiring a complex noise model.